The present invention relates generally to a method and system of processing diffusion weighted magnetic resonance (MR) data and, more particularly, to a tensor-free method and system of quantitative assessment of diffusion characteristics in a targeted tissue.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization”, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment M1. A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
It is generally well-known that MR imaging techniques may be exploited to non-invasively discriminate between normal and pathological tissues in a subject, such as a medical patient. One such MR imaging technique is predicated on the acquisition of diffusion-weighted MR data that enables a physician, radiologist, or other health care provider to assess diffusion anisotropy in a particular tissue region of a patient, such as in the human brain. Until recently, health care providers were unable to quantitatively assess diffusion. That is, MR imaging supported the acquisition and reconstruction of MR data that allowed a clinician to visually inspect and determine, from the reconstructed images, a qualitative measure of diffusion. Since assessment of diffusion was limited to a qualitative evaluation, the extent of the diagnosis process was limited to the experience and expertise of the health care provider as well as the quality of the data acquisition process. Moreover, patient movement, field inhomogeneities, and the like could negatively affect the data acquisition process thereby hindering a physician's or radiologists' ability to accurately assess diffusion when visually determining and making a diagnosis.
As a result, recently, quantitative approaches to diffusion weighted imaging have been proposed. With these quantitative approaches, a value of diffusion is determined that enables a clinician to quantitatively assess the diffusion in the tissue imaged to determine whether the tissue is normal or whether a pathology is present. Moreover, by quantitatively measuring diffusion, the degree of pathology may also be determined.
However, with some of these known quantitative approaches, a tensor-based approach is implemented. These conventional tensor-dependent approaches, however, significantly limit assessment of diffusion when diffusion extends or tracts along multiple directions in a particular imaging volume. Further, these conventional tensor-based techniques must make numerous assumptions regarding the diffusion data. As a result, the accuracy of the quantitative assessment is remarkably dependent on the assumptions made.
It would therefore be desirable to design a method and system of quantitatively assessing diffusion weighted data in diagnosing a tissue without making assumptions as required by a tensor-based model that is also applicable to voxels having multiple diffusion tracts.